Jia-Jie Zhu receives the Marie Curie Individual Fellowship

A prestigious junior scientist fellowship is awarded to the MPI-IS researcher

Our postdoctoral research scientist, Dr. Jia-Jie Zhu, has recently been awarded the Marie Sklodowska-Curie Individual Fellowship, one of Europe’s most prestigious grant awards for junior scientists.

Jia-Jie holds a joint Ph.D. degree in mathematics and statistics. He received doctoral training with Prof. William Hager at the University of Florida where he focused on optimization and statistical machine learning. Currently, Jia-Jie is working on his project in reinforcement learning (RL) and optimal control, focusing on the scalability and data efficiency in robotic control applications. In particular, he takes a unique approach of combining optimal control tools with reinforcement learning in applications such as model-based and deep RL, as well as developmental robotics where the group leader Georg Martius’s expertise lies.

“I draw my inspirations from two disciplines: optimal control and reinforcement learning. My goal is to produce research aiming to combine the wide applicability of RL and the rigor of optimal control. MPI-IS is the perfect place for such advancement to take place; we have in-house international experts in control and robotics whom I can collaborate with”. says Jia-Jie.

The Marie Sklodowska-Curie Individual Fellowship is one of Europe’s most prestigious grant awards for experienced researchers, who are looking to give their career a boost by working abroad. Applicants need a doctoral degree or at least four years’ full-time research experience. The grant provides the recipients with an allowance to cover living, travel and family costs. In addition, the EU contributes to the training, networking and research costs of the fellow, as well as to the management and indirect costs of the project.

MSCA Fellows come from a wide variety of disciplines – from physics to linguistics, and from health-sciences to mathematical modelling. In Jia-Jie Zhu´s case it is the interdisciplinary field of Machine Learning and Robotic Control: His research focuses on data-driven computational approaches to artificial intelligence and robotic control and how intelligent systems learn and act in various environments.

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems